At a global messaging platform processing billions of messages annually across 20+ brands, we were about to ship AI features to customers. The stakes were high: multi-jurisdictional regulatory exposure, legal liability, and brand reputation all on the line. Before we flipped the switch, I needed to know one thing: did Azure AI's content safety guardrails actually work?
They didn't.
What I found during red-team testing would have created serious commercial and legal exposure. This is the story of what I tested, what broke, how Microsoft responded, and why you should be doing this before you deploy any AI system to production.
Why Red-Team AI at All?
Most companies treat AI safety as an ethics conversation. They talk about responsible AI principles, establish governance committees, and write policy documents. That's fine, but it misses the point.
AI safety is a commercial risk management exercise. If your AI system generates harmful, illegal, or regulated content, you face:
- Legal liability — Content that violates laws in the jurisdictions you operate in
- Regulatory action — Fines, sanctions, or operational restrictions from regulators
- Brand damage — Public incidents that destroy customer trust
- Contract breaches — Violations of customer agreements or SLAs
When you're deploying AI across 20+ brands with different regulatory contexts, this isn't theoretical. It's a question of when, not if, something will go wrong. The only question is whether you find it first or your customers do.
What I Actually Tested
Azure AI provides content safety filters designed to catch harmful outputs before they reach users. Microsoft markets these as production-ready guardrails. I needed to verify that claim.
My testing focused on three areas:
Prompt Injection Attacks
Could I trick the AI into ignoring its safety instructions? I tested variations of jailbreak prompts, role-playing scenarios, and instruction override attempts. The goal was to see if the content filters could be bypassed through clever prompt engineering.
Content Boundary Testing
Where exactly were the lines? I systematically tested edge cases around regulated content categories: violence, self-harm, sexual content, hate speech, and regulated substances. Not to generate harmful content for its own sake, but to map the actual boundaries of what the filters would and wouldn't catch.
Multi-Turn Conversation Exploits
Could I escalate through a conversation to reach harmful outputs that would be caught in a single prompt? Many safety systems focus on individual messages but fail to track context across a conversation thread.
I also tested multilingual edge cases, since our brands operated across multiple languages and jurisdictions. Content safety in English doesn't necessarily translate to other languages.
What I Found
The filters failed in ways that would have created real commercial exposure.
I'm not going to publish the specific exploits — that's not the point, and it would be irresponsible. But I will tell you the categories:
- Prompt injection bypasses that allowed the system to ignore safety instructions
- Content boundary failures where regulated content passed through filters that should have caught it
- Context tracking gaps where multi-turn conversations could escalate to harmful outputs
- Language-specific vulnerabilities where non-English content evaded detection
These weren't edge cases that would never happen in production. These were realistic scenarios that any sufficiently motivated user could discover. And in a multi-brand deployment with millions of users, someone would have found them.
How Microsoft Responded
I escalated my findings to Microsoft's AI safety team. Their response was exactly what you'd want: fast, professional, and collaborative.
Within 48 hours, I was in direct contact with engineers on the Azure AI safety team. They took the findings seriously, reproduced the issues, and worked with us to implement mitigations before our go-live date.
Some of the fixes were configuration changes on our end — adjusting filter sensitivity and implementing additional validation layers. Others were platform-level improvements that Microsoft deployed to Azure AI itself.
The collaboration was excellent. Microsoft treated us as partners in improving their product, not as adversaries trying to break it. They understood that our success was their success, and they invested engineering time to make sure we could deploy safely.
Why Most Companies Skip This
Here's the uncomfortable truth: most companies don't red-team their AI systems before deployment.
Why not?
- Time pressure — Red-teaming takes time, and product teams are always under pressure to ship
- False confidence in vendor promises — If Microsoft says the filters work, why test them?
- Lack of expertise — Not every team has someone who knows how to red-team AI systems
- Fear of what they'll find — If you discover vulnerabilities, you have to fix them before shipping
None of these are good reasons. Time pressure doesn't make your legal liability go away. Vendor promises don't protect you from regulatory action. And finding vulnerabilities before deployment is cheaper than finding them in production.
What You Should Do
If you're deploying AI systems to production, here's my advice:
1. Red-team before go-live
Don't wait until after deployment. Budget time for security testing as part of your project plan. If you don't have internal expertise, hire someone who does.
2. Test realistic scenarios
Don't just test obvious attacks. Think about how real users might interact with your system, including adversarial users. Test in all languages you support. Test multi-turn conversations. Test edge cases.
3. Document everything
Keep records of what you tested, what you found, and how you mitigated it. If something goes wrong in production, you'll need to demonstrate that you did due diligence.
4. Work with your vendor
If you find issues, escalate to your vendor's security team. Good vendors will work with you. If they don't, that tells you something important about the platform you're using.
5. Implement defense in depth
Don't rely solely on your AI vendor's content filters. Add your own validation layers, monitoring, and circuit breakers. Assume the filters will fail eventually and plan for it.
The Bottom Line
AI safety isn't an ethics conversation. It's a commercial risk management exercise.
The guardrails that vendors provide are a starting point, not a finish line. They will fail in ways you don't expect, and those failures will create real business exposure.
Red-teaming before deployment isn't paranoia. It's due diligence. It's the difference between finding vulnerabilities in your test environment and finding them in production with customers, regulators, and lawyers watching.
We found critical issues before go-live. We worked with Microsoft to fix them. We deployed safely. That's not lucky — it's what happens when you treat AI safety as the engineering problem it actually is.
If you're deploying AI to production and you haven't red-teamed it, you're taking a risk you don't need to take. Do the work now, or deal with the consequences later.